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Record W4322627851 · doi:10.21203/rs.3.rs-2612153/v1

CD45 inhibition in myeloid leukaemia cells sensitizes cellular responsiveness to chemotherapy

2023· preprint· en· W4322627851 on OpenAlexfundno aff
Maryam Ahmed S. Al Barashdi, Ahlam Ali, Mary Frances McMullin, Ken Mills

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldImmunology and Microbiology
TopicImmune Cell Function and Interaction
Canadian institutionsnot available
FundersQueen's UniversitySultan Qaboos UniversityQueen's University Belfast
KeywordsMyeloid leukaemiaChronic myeloid leukaemiaCancer researchMyeloidChemotherapyChemistryCell biologyInternal medicineMedicineBiology

Abstract

fetched live from OpenAlex

Abstract Background Myeloid malignancies are a group of blood disorders characterized by the proliferation of one or more haematopoietic myeloid cell lineages, predominantly in the bone marrow and are often caused by aberrant protein tyrosine kinase activity. The protein tyrosine phosphatase CD45 is a transmembrane molecule expressed on all haemopoietic blood cells except that of platelets and red cells. CD45 regulates various cellular physiological processes including proliferation, apoptosis, and lymphocyte activation. The aim of this study was to investigate the role of CD45 in myeloid malignancies in terms of cellular growth, apoptosis, and response to chemotherapy to investigate alternative therapies with less toxicity and more efficacy. Methods The expression profile of CD45 was established in a panel of myeloid leukaemia cell lines and in peripheral blood cells from normal individuals, AML and MPN patients using western blot, flow cytometry, and PCR. CD45 KD was performed by using siRNA nucleofection and lipofectamine RNAiMAX technology. Bioinformatics study was performed using Partek Genomic Suit and Quadratic software for connectivity mapping. RNA sequencing was performed on CD45 KD cells and CD45 inhibited cells versus control cells. Apoptosis was studied by using Caspase-Glo™ Assay and western blotting. Drug combination studies between CD45 inhibitors and chemotherapy were performed and cytotoxicity was estimated by using Cell Titer-Glo® (CTG). Results The expression of CD45 on myeloid leukaemia primary cells and cell lines was heterogeneous with HEL and OCI-AML3 cells showing the highest level. Knockdown (KD) and/or inhibition of CD45 resulted in increased cellular sensitivity to cytarabine and ruxolitinib. Bioinformatics analysis identified of alendronate, allopurinol, and balsalazide as CD45 inhibitors along with genes whose expression was correlated with CD45 expression such as JAK2, ACTR2, THAP3 Serglycin and PBX-1 genes. Conclusions CD45 inhibition could be explored as a potential therapeutic partner for treatment of myeloid malignancies in combination with chemotherapy such as cytarabine. Identification of alendronate, allopurinol, and balsalazide, licensed drugs, could be repurposed as CD45 inhibitors at non-toxic concentrations which effectively increases sensitivity to cytarabine and ruxolitinib at low doses. Therefore, combining CD45 inhibitor drugs with low dose chemotherapy can be beneficial in treating myeloid leukaemia and can provide an alternative therapy characterized by less toxicity and more efficiency especially for elderly patients and those showing chemotherapy resistance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.051
GPT teacher head0.347
Teacher spread0.296 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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